Credit Risk Measurement

There are various credit risk measurement methodologies today. It is a challenge for managers to see how these can be used to resolve the problem of credit risk measurement.

Executives of banks, financial institutions, and business organizations should be able to address the problem of credit risk measurement in order to protect their firms from credit loss.

Credit risk commonly refers to that risk that lenders face in case an obligor or creditor fails to settle or repay his debt. Credit risk measurement is, therefore, crucial as it helps determine and assign a quantitative value with regards to the capacity of a creditor to settle his account, as well as the default probability involved. Financial analysts consider credit risk more complex to measure compared to market risk for several reasons. It is also as difficult to model as market risk. One of the contributing factors for this is the absence of a liquid market that is capable of assigning a value of credit risk for a specific creditor and tenor. Another factor is that default probabilities can be distorted by inferring default rates that can be determined from historical public credit scores and subjective credit approval process. Lastly, default correlations are generally difficult to measure and observe, making it more complex to determine aggregate credit risk.

Company executives of banks and lending institutions now see the need to develop an accurate credit risk measurement process in order to balance rewards and risks. They should ensure that loan products do not have high interest rates, or else they stand the chance to lose a customer. At the same time, these loan products should not be too low to the point that they will translate to minimal profit margin or losses. In credit risk measurement, concepts like recovery rate, default exposure, unexpected losses, and default probability are just some of the concepts that are normally used. These measures are usually taken into account since small variations in credit risk measurement could have huge implications on credit risk estimates. Generally, consumer lenders use borrower credit scorecards as basis of improving portfolio management and decreasing underwriting costs. Nevertheless, the development of commercial credit risk measurement methodology is hampered by infrequency of defaults, as well as limited historical data that is available.

When measuring credit risk, two components are generally assessed. These components are unexpected and expected loss. According to the formula commonly used by banks, expected loss is a product of exposure, loss given default (LGD), probability of default (PD), and Exposure at Default (EAD). In essence, expected loss is a measure of average losses incurred over a given risk period. Unexpected loss (UL), on the other hand, is a measure of what might go wrong over a given period of time. To measure this, financial institutions usually employ a credit value-at-risk approach. The UL metric gives lenders an idea of potential volatility of a credit portfolio.

Most lending companies have departments that are specially organized to deal with the problem of credit risk measurement. The credit scorecard approach is widely used in the industry. Others also use specialized metrics and key performance indicators for measuring credit risk as part of their evaluation of clients seeking to benefit from their loan products.

Managers of financial institutions like banks need to familiarize themselves with metrics and performance indicators for banking industry since these will help them in making profitable investment decisions.

To efficiently manage banks and financial institutions, identifying relevant metrics and performance indicators for the banking industry is very crucial.

Banks exist to provide consumers and businesses financial services. They are financial institutions that receive, transfer, pay, collect, exchange, lend, safeguard, and invest money in behalf of its customers or depositors. Services provided by banks are extremely important in free market economies, like United States and Canada. Two of their primary functions are to supply customers with mediums of exchange like checking accounts, credit cards, and cash; and to accept money from depositors and lending this to borrowers. These two functions allow an economy to expand and grow.

In the face of tight competition and changing customer loyalties, the use of key performance indicators (KPIs) and relevant metrics will certainly help bank managers and executives make good corporate decisions that will help them achieve their organizational objectives. KPIs are quantifiable measures that can give managers a quick assessment of performance. A common dilemma for managers is to identify which among the many metrics that can be easily obtained can be used as basis of organizational performance. The metrics that will be used as KPIs should be relevant and should yield information that will be extremely useful for managers who are running banks. As is discussed in many textbooks, these indicators should pass the SMART acronym criteria. They should be specific, measurable, achievable, relevant or result-oriented, and time-bound.

Key performance indicators may be financial or nonfinancial, and are usually based on the organizational structure and operating strategies of a bank. Liquidity ratios are often used and considered as crucial KPIs by many financial institutions. According to the Uniform Bank Performance Report, there are twelve liquidity ratios that banks can use as KPIs. The amount of uninvested funds is another KPI that will help bank managers determine the amount or percentage of bank funds that are fully invested and income-generating. Another indicator that will come handy for bank managers is the amount of loan commitments the bank has from the beginning to the end of a certain period. A table indicating these figures will illustrate activity. Moreover, to obtain profitability information, it is a good idea to monitor outstanding loans, new loans, ending total loans, and principal reduction. Other factors that can be treated as KPIs are ratio of active depositors against dormant depositors, number of depositors per branch, number of closed accounts, and number of issued credit cards monthly.

Aside from the metrics previously mentioned, rate of credit risk and default risk rate should also be given close attention. This is for the reason that credit risk is one of the major challenges that banks and lending institutions worldwide face. In fact, this is very crucial and may lead to bankruptcy, if left unattended. Efficient credit risk measurement tools should be used in order to maintain credit risk level well within an acceptable range. Despite the differences in management styles and organizational structures of banks, the metrics and performance indicators for banking industry previously mentioned should help bank managers accurately assess their performance.

The new version of Balanced Scorecard Designer was released, with this version business users will get more opportunities of integration their scorecards, metrics and KPIs with databases of business system.

There is a new feature called “SQL Indicator”, with this function users can access data from business databases, such as ERP, CRM, MPC or some special banking database.

There are several integration ways, which will help business users to connect scorecard or KPI they design with real data from banking database.

There is a need for bank managers to measure credit risk through a scorecard approach. This method helps determine which lending opportunities to take advantage and which to ignore.

Nowadays, it pays very well for business organizations to measure credit risk with scorecard. With this approach, company executives would be able to effectively manage any credit risk encountered.

There are various tools that can be used to measure credit risk. Aside from scorecards, other important tools are key performance indicators (KPIs) and metrics. To be able to thoroughly understand how these tools work, the concept of credit risk should be understood. The term “credit risk” is commonly defined as the risk of loss because of the inability of a debtor to pay for any line of credit or loan. Credit risk can be categorized into the following types, namely, the credit risk faced by lenders or consumer credit risk, credit risk faced by lenders to business clients, credit risk faced by businesses, and credit risk faced by individuals. In measuring consumer credit risk, credit scorecards are often used to rank existing and new customers according to the likelihood that they would be able to pay. Usually, higher interest rates are given to customers that are considered as high credit risks. Moreover, credit limits are also set especially for products like overdraft lines and credit cards. The second type of credit risk is typically applicable for lenders of business organizations. Generally, lenders determine the cost and benefits of a loan depending on the interest rate assessed and level of credit risk. Aside from controlling the interest rates, lenders are also afforded credit protection by protective clauses that are often integrated in loan agreements. Some lenders also opt to take advantage of credit derivatives like a credit default swap. Meanwhile, credit risk faced by business is that risk faced by businesses especially when they do not require cash payment for their products and services. Finally, credit risk for individuals is the risk that consumers face as bank depositors or parties of commercial transactions. To help minimize the repercussions of this credit risk type, governments usually adopt legal mechanisms to protect consumers like bank deposit insurance.

Compared to market risk, experts consider credit risk difficult to measure. There are several reasons behind this; but the most evident of which is the absence of a liquid market that makes it impossible to tag price to credit risk for the obligor and loan tenor. Nevertheless, there are simple credit risk measurement concepts that can be easy to determine, such as unexpected loss, default probability, exposure at default, and recovery rate.

An increasing number of companies, especially lenders, measure credit risk with scorecard. Credit scorecards are mathematical models that are designed to assign a quantitative value to a customer’s behavior with regards to his credit position. This tool computes and determines the financial value of a loan, given its risk level from the viewpoint of the debtor. Generally, credit scoring is done by deriving information from a certain database that contains observations and data on previous clients with loan defaults. Default probabilities are then placed on a scale with credit score. Modern credit scorecard techniques like logistic regression, hazard rate modeling, and reduced form credit models are now employed to make credit risk measurement more efficient.

Credit risk is a major problem faced by financial institutions. To generate revenue from lending opportunities, managers of these firms should be able to measure and control credit risk.

Due to the immense need of several business organizations, especially lending and financial institutions to measure and control credit risk, various credit risk management methodologies have been developed.

Since time immemorial, controlling credit risk had been a challenge for market regulators and risk managers. In fact, international regulation regarding the credit risks of banks had been instituted way back in 1998 to address this problem. It had also been revealed that financial institutions face serious problems due to their inattention and inability to control credit risk levels. This is usually evident through the lenient credit standards used, inattention to economic factors that may lead to credit standing deterioration of debtors, and poor portfolio management.

Understanding the concept of credit risk management is a must because this will help managers identify which lending opportunities to reject and which to pursue. Credit risk is basically the potential that debtors or borrowers are unable to repay loans and other lines of credit, or fail to perform their obligations as previously agreed. Through efficient credit risk measurement tools, managers should be able to monitor and keep credit risk levels within a desirable range. This is very similar to the goal of credit risk management, which is to maximize risk-adjusted return rate by limiting credit risk exposure within the desired parameters.

Credit risk exposure, to this day, is the biggest dilemma of banks worldwide. Based on historical data and past experiences, banks and other financial institutions are now more concerned with identifying, measuring, monitoring, and controlling credit risk. At the same time, these firms make sure that they have adequate assets and capital against these risks.

Perhaps the most common credit risk measurement tool used today is the credit scorecard. This statistics-based model is designed to attribute or assign a score or number to a customer or account indicating the predicted probability of certain customer behavior. In determining this score, various data sources can be used, including data indicated in the application form and data obtained from credit reference agencies. The application scorecard, in particular, is the most popular scorecard type. This is the type generally used when customers apply for a new loan or credit product. The score used in this tool is a three or four digit number that is consistent to the natural logit or probability of that customer turning “bad” or unable to perform its obligation. Other scorecard types, like behavioral scorecards and propensity scorecards, are also used to predict the chances of a current account turning “bad.”

Aside from credit scoring, other credit risk measurement and control methodologies, like reduced form credit models, logistic regression, and hazard rate modeling are now starting to gain popularity. Today, a number of managers are already using specialized software tools to accurately predict credit risk levels and potential losses. These also compute the capital reserves required against credit risks. The Internet is a wealthy resource for these products. You can easily find these products on the many online stores all over the web. Meanwhile, some financial institutions prefer to hire third-party firms to monitor and help them measure and control credit risk.

For banks to generate income from loan products and other lines of credit, managers should be able improve credit risk evaluation with KPI that quantitatively measure exposure to credit risk.

It is a challenge for many corporate executives and risk managers to improve credit risk evaluation with KPI or key performance indicators.

According to the Principles for the Management of Credit Risk released by the Basle Committee on Banking Supervision released in 1999, credit risk is the potential that a counterparty or bank borrower will fail to perform his obligations as previously agreed. In light of this, the goal of credit risk management is to maximize the risk-adjusted rate of return of a bank or financial institution by limiting credit risk exposure. This describes the urgency for banks to effectively manage credit risk in their loan portfolio and transactions. Moreover, this also establishes the need for bank managers to understand the relationship between other types of risks and credit risk. Since the early days of banks, lenient credit standards for counterparties and borrowers, inattention to economic factors that will affect consumer behavior, and poor portfolio and risk management had been identified as the major causes of banking dilemmas. Particularly for banks, loans and other lines of credit are the biggest sources of credit risk. For this reason, banks are expected to make use of efficient credit risk management tools in order to limit risk exposure. Perhaps the foremost manifestation of this is the increasing amount of effort that bank managers put into identifying, measuring, controlling, and monitoring credit risk.

Fortunately, it is now possible for financial institutions to perform credit risk evaluation conveniently due to the onset of modern technology. Some advanced software applications have now become essential tools to support decision-making when it comes to which lending opportunities to pursue and which to ignore. Aside from accurately predicting potential losses to be incurred with high credit risk, these software tools also calculate the amount of assets or capital reserves needed to satisfactorily minimize risk. Moreover, industry experts and risk managers now see the wisdom behind using key performance indicators when measuring and controlling credit risk.

According to credit professionals, credit risk management could be efficiently implemented with thorough understanding and proper use of indicators, like probability of default (PD), Loss Given Default (LGD), Exposure at Default (EAD), Expected Loss (EL), and Unexpected Loss (UE). Probability of Default (PD), or Expected Frequency Default (EFD) is a frequency measure that describes the risk that a borrower may not be able to give full and prompt payment. Loss Given Default (LGD), on the other hand, is a new measurement concept that describes the risk that loss is incurred if there is already a default event. This concept is also labeled as loss in the event of default (LIED). Exposure at Default (EAD), meanwhile, is that measure which quantitatively defines expected drawn risk exposure during the time of default. Expected Loss (EL), as per its name, is a measurement of losses that are anticipated over a given risk period. In contrast, Unexpected Loss (UL) measures what may go wrong in a loan transaction. Aside from these concepts, liquidity ratios can also be used as success indicators. Thorough understanding of these concepts should help bank managers improve credit risk evaluation with KPI.

Taking managerial decisions keeping growth, risk and returns in mind are tough and tricky for the best banking brains, but performance metrics can help…

Business decisions are getting tougher and tougher in a world of cut-throat competition and swaying customer loyalties. In a day and time when industry follows no fixed trend and age-old business practices are failing for no reason at all, making management decisions has become extremely tough. However the use of performance metrics and key performance indicators can actually help managers take better decisions and make calculated choices. As far as the banking sector is concerned, banking performance metrics may vary from performance metrics in other organizations. At the same time, different banks may choose to focus on different performance metrics based on their goals. Banking performance metrics about key focus areas in your company, based on the policies, vision and aims of your own organization can help you in analyzing current situations and determining future course of action in an extremely objective and calculated manner.

No matter what your goal is or what kind of banking policies you follow, the use of performance metrics in assessing the overall performance of your organization can definitely help you in improving the overall functioning of your bank and in pushing your profits. Since most banking sector decisions involve trade-offs between risk and returns, almost every bank is into calculating the newly evolved EVA (economic value added) and RAROC (risk-adjusted return on capital). On the other hand, due to the extreme importance that is being given to customer relations nowadays, formulae for performance metrics calculating customer satisfaction are being developed every other day.

In order to help the performance of their banks, most managers are nowadays using specialized software tools or calculators for determining their performance metrics. Other banks simply employ the services of consultancies and financial firms who assess performance in different areas and provide detailed metric values. In either case, banks today need to get data on key performance indicators in all sectors ranging from customer satisfaction, growth, employee turnover and performance, productivity, profitability and risk management. Some of the main performance metrics that almost all banks need to focus on are return on capital employed, overhead cost ratio, ,return on operating capital, return on average assets, operating margin, fee income level, non-interest income level and different types of capital ratios.

Many companies also use the balanced scorecard method for gathering and calculating their key performance metrics. The balanced scorecard is a tool that provides formulae for calculating different performance metrics for different organizations and different operational situations. Industry experts may use varying terms for denoting performance metrics like business activity monitoring, business intelligence, business performance management and enterprise metrics management but the plain and simple truth is that nobody is making any kind of decisions without first checking out their performance metrics.

Successful banking is impossible without continuously assessing performance variables and acting upon what these numbers tell you. Whether you run a small bank or a worldwide chain, you need to work with banking performance metrics before taking any decisions because performance metrics are the best decision making variables that you can get your hands on today.

There are different approaches to credit risk measurement, most of them focus on such performance management tools as KPIs, Metrics, Scorecards. On this web-site we are reviewing the most popular tools and methods to measure credit risk.

There are three popular Balanced Scorecard products that are useful if you want to get started with credit risk measurement, this metrics are:

If you will purchase these 3 metrics together, then you will have 30% discount for your order.